High-Performance Feature Selection Model for Network Intrusion Detection System

Network intrusions detection is a continuous vigilant task and to efficiently analyze the traffic in the corporate network to detect network intrusions. The efficiency of the Network Intrusion Detection System (NIDS) performance can be improved by adopting feature selection or reduction process to suit the present day high speed real time networks. This work is focused on identifying the key features of the audit dataset used to build an efficient light-weight NIDS. The NSL KDD dataset is used in this work titled Attribute Richness Based Feature Selection (ARFS) in order to analyze its performance.The obtained results are compared with the Correlation-based Feature Selection (CFS) and Information Gain (IG) feature selection methods. The proposed feature selection method produced better detection rate comparatively.